Method of automatic processing of a speech signal

ABSTRACT

Method of automatically processing a speech signal which comprises the steps of:
         determining a sequence of probability models corresponding to a given text;   determining a sequence of acoustic strings corresponding to the diction of the given text;   aligning between the sequence of acoustic strings and the sequence of models; and   determining a confidence index of acoustic alignment for each association between a model and an acoustic segment.       

     Each determining step of an alignment confidence index is carried out at least from a combination of the model probability, a priori model probabilities and the average duration of occupancy of the models.

TECHNICAL FIELD

The present invention relates to a method of automatic processing of aspeech signal including a step of alignment between a model of a textand a speech signal corresponding to the diction of this text.

Such methods are used, for example, within the framework of speechsynthesis or also the determination of learning databases for voicerecognition systems.

BACKGROUND TO THE INVENTION

With reference to FIG. 1, a method of automatic processing according tothe prior art will be described within the framework of voice synthesis.

This method includes an automatic step 2 of determination of a sequenceof probability models which represent a given text.

Conventionally, the probability models used are a finite number ofso-called hidden Markov models or HMM which describe the probability ofacoustic production of symbolic units of a phonological nature.

At the same time as step 2, the method includes a step 4 ofdetermination of a sequence of digital data strings corresponding to thediction of the same given text, or acoustic strings.

The method then includes a step 6 of alignment between the sequence ofacoustic strings and the sequence of models.

Thus each symbolic unit of phonological order represented by one orseveral models has associated with it a sub-sequence of acoustic stringsknown as an “acoustic segment”.

For example, these associations between a symbolic unit and an acousticsegment are memorised individually in order to permit subsequent speechsynthesis by generating a sequence of acoustic strings corresponding toa text other than the aforementioned given text.

However, variations may appear at the time of the alignment step 6resulting in particular from differences between the speech signal asreally pronounced and the sequence of models corresponding to atheoretical pronunciation.

In fact, step 2 of determination of a sequence of models associates asingle model sequence with a given text.

However, the diction of this text may give rise to different speechsignals due to the influence of the speaker. In particular, phoneticunits or phonemes may be associated with each other as in the case ofliaisons, or also other phonemes may be omitted or lengthened.

Such variations may involve the association of a model with an erroneousand/or displaced acoustic segment, thus introducing an error ofalignment into the following acoustic segments.

The result of these variations is the necessity of introducing, for eachassociation between an acoustic segment and one or several models, aconfidence index during step 8 which enables a probability score to beattributed to each association.

However, in the methods according to the prior art, these confidenceindices calculated for each model are not very precise.

In particular, these confidence indices are calculated essentially fromthe probabilities of transition from one model to the other. Thus theseconfidence indices are directly calculated for a segment of acousticstrings involving a low degree of precision.

Conventionally, these confidence indices only permit the rejection ofcertain associations which are corrected manually by specialists duringa long and costly correction step 10.

It is therefore apparent that in the methods according to the prior artthe precision of the confidence indices is insufficient, thus making theprocessing methods long and costly due to the necessity of humaninterventions for corrections.

The object of the present invention is to remedy this problem bydefining an automatic method of processing which includes a confidenceindex with increased precision.

SUMMARY OF THE INVENTION

The invention relates to a method of automatic processing of a speechsignal comprising:

-   -   an automatic step of determination of at least one sequence of        probability models coming from a finite directory of models,        each sequence describing the probability of acoustic production        of a sequence of symbolic units of a phonological nature coming        from a finite alphabet, the said sequence of symbolic units        corresponding to at least one given text and the said        probability models each including an observable random process        corresponding to the acoustic production of symbolic units and a        non-observable random process having known probability        properties, so-called Markov properties;    -   a step of determination of a sequence of digital data strings,        known as acoustic strings, representing acoustic properties of a        speech signal;    -   a step of alignment between the said sequence of acoustic        strings and the said sequence of models, each model being        associated with a sub-sequence of acoustic strings, forming an        acoustic segment, and each value of the non-observable process        of each model being associated with a sub-sequence of acoustic        strings forming an acoustic sub-segment in order to deliver a        sequence of non-observable process values associating a value        with each acoustic string, known as an aligned sequence; and    -   a step of determination of a confidence index of acoustic        alignment for each association between a model and an acoustic        segment, known as a model alignment confidence index, and        corresponding to an estimate of the probability a posteriori of        the model given the observation of the corresponding acoustic        segment, known as the a posteriori model probability.

Each step of determination of an alignment confidence index for a modelcomprises the calculation of the value of the said index at least from acombination of:

-   -   the probability of observation of each acoustic string given the        value of the non-observable process, known as the model        probability and determined from known mathematical properties of        the model and of the said sequence of acoustic strings;    -   probabilities of production a priori of all the models of the        said directory, independently of one another, known as the a        priori model probabilities; and    -   the analytical estimation of the average duration of occupancy        of the values of the non-observable process of the model.

According to other characteristics:

-   -   each step of determination of an acoustic confidence index for a        model includes a sub-step of determination of the estimate of        the a priori probability of each value of the non-observable        process of the model, known as the a priori value probability,        carried out on the basis of the said analytical estimation of        the average duration of occupancy of the values of the        non-observable process of the model;    -   each step of determination of an alignment confidence index for        a model includes a sub-step of determination of a confidence        index for each acoustic string forming the acoustic segment        associated with the said model and a sub-step of combination of        the confidence indices of each string of the said segment in        order to deliver the said confidence index of the said model;    -   each sub-step of determination of a confidence index for a given        string includes:        -   a sub-step of initial calculation combining the model            probability, the a priori model probability of the model in            progress and the average duration of occupancy of the            non-observable values for all the values of the            non-observable process of the said aligned sequence and of            the model in progress;        -   a sub-step of calculation of the product of the model            probability, the a priori model probability and the a priori            value probability, carried out for each value of the            non-observable process of all the possible models in the            said finite directory of models; and        -   a sub-step of summation of all the said products for all the            possible models of the said finite directory of models in            order to deliver the said confidence index of the said given            acoustic string from the results of the said sub-steps;    -   the said automatic step of determination of a sequence of        probability models corresponding to a given text includes:        -   a sub-step of acquisition of a graphemic representation of            the said given text;        -   a sub-step of determination of a sequence of symbolic units            coming from a finite symbolic alphabet from the said            graphemic representation; and        -   an automatic sub-step of modelling of the said sequence of            units by its breakdown on a base of the said probability            models in order to deliver the said sequence of probability            models;    -   the said modelling sub-step associates a single probability        model with each symbolic unit of the said sequence of symbolic        units;    -   the said step of determination of a sequence of digital strings        includes:        -   a sub-step of acquisition of a speech signal corresponding            to the diction of the said given text, adapted in order to            deliver a sequence of digital samples of the said speech            signal; and        -   a sub-step of spectral analysis of the said samples in order            to deliver a breakdown of the frequency spectrum of the said            speech signal on a non-linear scale, the said breakdown            forming the said sequence of acoustic strings;    -   the said sub-step of spectral analysis corresponds to a sub-step        of Fourier transformation of the said speech signal, of        determination of the distribution of its energy on a non-linear        scale by filtering, and of transformation into cosine;    -   the said step of alignment between the said sequence of acoustic        strings and the said sequence of models includes:        -   a sub-step of calculation of a plurality of possible            alignments each associated with a relevance index; and        -   a sub-step of selection of a single alignment amongst the            said plurality of possible alignments;    -   the said sub-step of determination of a plurality of possible        alignments comprises the calculation of at least one optimum        alignment, as determined by a so-called Viterbi algorithm;    -   it also includes a step of local modification of the said        sequence of models as a function of the said alignment        confidence indices determined for each model of the said        sequence of models;    -   the said step of local modification comprises a sub-step of        deletion of a model from the said sequence of models;    -   the said step of local modification includes a sub-step of        substitution of a model of the said sequence of models by        another model;    -   the said step of local modification includes a sub-step of        insertion of a model between two models of the said sequence of        models;    -   the said steps of alignment and of calculation of a confidence        index are repeated after each step of local modification of the        said sequence of models;    -   the said step of determination of at least one sequence of        models is adapted for the determination of a sequence of models        corresponding to a given text, and in that the said sequence of        acoustic strings represents properties of a speech signal        corresponding to the locution of the said same given text;    -   the said step of determination of sequences of models is adapted        for the determination of a plurality of sequences of models each        corresponding to a given text, and in that the said sequence of        acoustic strings represents properties of a speech signal        corresponding to the locution of any text whatsoever, the said        method including a step of selection of one or several sequences        of models amongst the said plurality for carrying out the said        step of determination of confidence indices;    -   the said models are models of which the observable processes        have discrete values, the values of the non-observable processes        being the states of these processes;    -   the said models are models of which the non-observable processes        have continuous values.

BRIEF DESCRIPTION OF DRAWINGS

The invention will be better understood upon reading the followingdescription which is given solely by way of example and with referenceto the accompanying drawings, in which, apart from FIG. 1 which hasalready been mentioned and shows a flow chart of a method of automaticprocessing according to the prior art within the framework of voicesynthesis:

FIG. 2 shows a flow chart of a method of processing according to theinvention within the framework of voice synthesis; and

FIG. 3 shows the detail of specific signals in the course of the methoddescribed with reference to FIG. 2.

DESCRIPTION OF PREFERRED EMBODIMENT

The method according to the invention described in FIG. 2 includes astep 20 of automatic determination of a sequence of probability modelsrepresenting a given text.

In the described embodiment, this step 20 includes a sub-step 22 ofacquisition of a symbolic representation of a given text, such as agraphemic or orthographic representation.

For example, this graphemic representation is a text drawn up with theaid of the Roman alphabet, designated by the reference TXT in FIG. 3.

The method then includes a sub-step 24 of determination of a sequence ofsymbolic units of a phonological nature of a finite alphabet from thesaid graphemic representation.

Such a sequence of symbolic units, denoted by the reference U in FIG. 3,is for example composed of phonemes extracted from a phonetic alphabet.

This sub-step 24 is carried out automatically by means of conventionaltechniques in the prior art, such as phoneticisation or other suchtechniques.

Thus for example the text “monsieur” in the French language isrepresented at the end of sub-step 24 by the sequence of phonetic units:[m]-[□]-[s]-[j]-[ø].

In particular this sub-step 24 implements a system of automaticphoneticisation using databases and permitting the breakdown of any textwhatsoever on a finite symbolic alphabet.

The step 20 then includes an automatic sub-step 26 of modelling of thesequence U of phonetic units by its breakdown on a base of probabilitymodels of hidden Markov models, commonly known as HMM.

In the described embodiment, the models of the sequence are referencedH₁ to H_(N) in FIG. 3 and are discrete models each including anobservable random process corresponding to an acoustic production and anon-observable random process designated Q and having known probabilityproperties called Markov properties, according to which the realisationof the future state of a random process only depends upon the presentstate of this process.

These models are defined previously, for example with the aid of neuralnetworks which make it possible to determine all of their parameters andin particular probabilities of retention in a given hidden state as wellas probabilities of transition between the final hidden state of a modeland the initial hidden state of another model.

The known mathematical properties of the Markov models make it possibleto determine the probability of observation of an acoustic productiondesignated O_(t), given the state of the non-observable process Q, knownas the model probability, denoted by P_(m) and corresponding to:P _(m) =P(O _(t) \Q _(t))

It will be recalled that such an expression is a conditional probabilityand corresponds to the probability of observation of the random variableO_(t), it being supposed that a given state Q_(t) of the random processQ has been produced.

These Markov models come from a finite directory including for example36 difference models referenced λ₁ to λ_(I) and are associated with thesymbolic units of the symbolic alphabet referred to previously.

In the described embodiment, each symbolic unit is associated with asingle hidden Markov model, such that the sequence U of phonetic unitsmakes it possible to determine directly a sequence H₁ to H_(N), denotedH₁ ^(N), of hidden Markov models describing the probability of acousticproduction of the sequence U of symbolic units.

Thus the step 20 permits the automatic determination of a sequence H₁^(N) of models corresponding to the automatic phoneticisation of a giventext.

At the same time as the step 20 of determination of the sequence H₁ ^(N)of models, the method includes in a conventional manner a step 40 ofdetermination of a sequence of digital strings, known as acousticstrings, representing acoustic properties of a speech signalcorresponding to the diction of the given text TXT.

In the described embodiment, this step 40 includes a sub-step 42 ofacquisition of a speech signal, identified by the reference s(t) in FIG.3 and corresponding to the diction of the given text TXT.

This sub-step 42 permits the acquisition of the temporal form of thespeech signal s(t) which is numbered and sampled such that the sub-step42 delivers a sequence of digital samples of the speech signal s(t).

As has been stated previously, the speech signal s(t) is directly linkedto the characteristics of diction of the speaker such that significantvariations can appear between different dictions and that a plurality ofacoustic signals can be considered as representing the same text TXT.

The step 40 then includes a sub-step 44 of spectral analysis of thedigital samples of the speech signal s(t) in order to deliver abreakdown of the frequency spectrum thereof.

In a conventional manner, this spectral analysis is an analysis known as“MFCC” (Mel Frequency Cepstrum Coefficient) which takes account of thenon-linear properties of the auditory perception and of a deconvolutionbetween the acoustic wave and the characteristics of timbre.

In the described embodiment, this analysis is carried out on a slidingwindow of the Hamming type, the result of which forms a sequence,referenced O₁ ^(T) in FIG. 3, of acoustic strings or acoustic vectorsreferenced O₁ to O_(T).

The sub-step 44 of spectral analysis corresponds for example to aFourier transformation of the speech signal s(t), to a determination ofthe distribution of its energy on a non-linear scale by filtering, thento a transformation into cosine.

The method then includes a step 60 of alignment between the sequence O₁^(T) of acoustic strings and the sequence H₁ ^(N) of probability models.

In particular, this step 60 of alignment permits the selection of anoptimum alignment in the sense of the so-called Viterbi algorithm.

Thus this alignment step 60 includes a sub-step 62 of calculation of aplurality of possible alignments, each associated with a likelihoodindex and a sub-step 64 of selection of a single alignment amongst thesaid plurality of possible alignments.

Such alignment techniques are known in the prior art and make itpossible to deliver a sequence of labelled acoustic strings such thateach model H_(n) of the sequence of models H₁ ^(N) is associated with asub-sequence O(H_(n)) of acoustic strings forming an acoustic segment.

Equally, each state of the non-observable process Q of each model H_(n)is associated with a sub-sequence of acoustic strings forming anacoustic sub-segment as shown with reference to FIG. 3.

Thus a start label and an end label are determined for each acousticsegment O(H_(n)) of the sequence O₁ ^(T) associated with a given modelH_(n) of the sequence H₁ ^(N).

This step 60 also makes it possible to deliver a sequence {tilde over(Q)}_(t) of non-observable states, called an aligned sequence,associating with each acoustic string O_(t) a given non-observable stateof a given model, denoted q_(j) ^(i) and corresponding to the j^(th)state of the i^(th) model of the sequence as shown in FIG. 3.

The method then includes a step 80 of determination of a confidenceindex of acoustic alignment for each association between a model H_(n)and an acoustic segment O(H_(n)).

This confidence index is called the model alignment confidence index,denoted I_(n), and corresponds to an estimate of the a posterioriprobability of the model given the observation of the correspondingacoustic segment denoted P_(mp) and corresponding to:P _(mp) =P(H _(n) \O(H _(n)))

Within the scope of the invention, each step 80 of determination of analignment confidence index I_(n) for a model H_(n) is carried out on thebasis of a combination of:

-   -   the probability of observation of each acoustic string given the        value of the non-observable process of the corresponding model,        that is to say the model probability P_(m) defined previously;    -   probabilities of producing a priori all the models λ₁ to λ_(I)        of the directory, independently of one another, known as a        priori model probabilities and denoted P(λ_(i)); and    -   the average time of staying at each of the states q_(j) ^(i) of        the model H_(n), denoted d(q_(j) ^(i)), calculated from        characteristic parameters of the model H_(n) and in particular        the parameters of transition between the non-observable states.

The probability of models P_(m) is determined from the known probabilityproperties of the model H_(n) and the observed sequence of acousticstrings O₁ ^(T).

The a priori model probabilities P(λ_(i)) are for example estimatedpreviously by counting the occurrences of phonemes from graphemic and/orphonetic transcriptions.

The average time of staying makes it possible in particular to estimatethe a priori probability of each value or state of the non-observableprocess Q of a model H_(n), known as the a priori value probability anddenoted P_(vp), which is expressed in the form of conditionalprobabilities by:P _(vp) =P(q _(j) ^(i)\λ_(i))and which corresponds to the a priori probability of being in a givennon-observable state referenced q_(j) of a given model λ_(i), denotedq_(j) ^(i), as was described previously.

The sequences being in relations of temporal order, the probabilitiesP_(vp) can be expressed in an analytical manner by the ratio between theaverage time passed on a state q_(j) ^(i), denoted d(q_(j) ^(i)), andthe average occupation time of the model λ_(i), denoted d(λ_(i)) andcorresponding to the sums of the average times of staying at each of thestates of which it is composed.

The following general analytical relation may then be written:

$P_{{??}\; p}\#\frac{\overset{\_}{d}\left( q_{j}^{i} \right)}{\overset{\_}{d}\left( \lambda_{i} \right)}$

As a function of the embodiments, the method of the invention canreceive the P_(vp) probabilities directly, for example calculatedpreviously and stored in a memory, or it can receive the estimatesd(q_(j) ^(i)) of the average duration of occupation of thenon-observable states of the processes of the model and effect thecalculation during a sub-step of determination of the a priori valueprobability P_(vp).

It then appears that the confidence index I_(n) can be expressedaccording to the following relation:

$I_{n} = {\log\left\lbrack {\prod\limits_{t = {b{(n)}}}^{e{(n)}}\;\frac{\left. {{P\left( O_{t} \right.}{\overset{\sim}{Q}}_{t}} \right)\frac{\overset{\_}{d}\left( {\overset{\sim}{Q}}_{t} \right)}{\overset{\_}{d}\left( H_{n} \right)}{P\left( H_{n} \right)}}{\left. {{\sum\limits_{i = 1}^{I}\;{\sum\limits_{j = 1}^{J{(i)}}{{P\left( O_{t} \right.}Q_{t}}}} = q_{j}^{i}} \right)\frac{\overset{\_}{d}\left( q_{j}^{i} \right)}{\overset{\_}{d}\left( \lambda_{i} \right)}{P\left( \lambda_{i} \right)}}} \right\rbrack}$

In this relation {tilde over (Q)}_(t) corresponds to the instant of thealigned sequence delivered at the end of the sub-step 64 and therefore,in the described embodiment, to an optimum sequence of states in thesense of the Viterbi algorithm extending between the instants t=b(n) andt=e(n) corresponding respectively to the start and the end of thesequence of observation O(H_(n)).

Since the term d({tilde over (Q)}_(t)) corresponds to the averageduration of the state at the instant t of the aligned sequence and theterm d(H_(n)) corresponds to the average duration of the n^(th) model ofthe sequence H₁ ^(N), they are both obtained from the average durationof occupation of the non-observable states denoted d(q_(j) ^(i)) in ageneral manner.

Finally, the index i makes it possible to run through the models λ₁ toλ_(I) of the directory of models and the index j makes it possible torun through the non-observable states 1 to J(i) of each model.

In order to implement this relation, the step 80 includes a sub-step 82of initial calculation in the course of which the numerator of therelation is calculated for a given string.

In the course of this sub-step 82 the model probabilityP_(m)=P(O_(t)\{tilde over (Q)}_(t)) is combined with the a priori modelprobability of the model in progress P(H_(n)), the average duration ofoccupancy of the aligned sequence d({tilde over (Q)}_(t)) and theaverage duration of the model in progress d(H_(n)).

The step 80 then includes a sub-step 84 of calculation of the product ofthe probability of models P_(m) with the a priori model probabilityP(λ_(i)) and the a priori value probability P_(vp). This sub-step 84 iscarried out for all the non-observable states of all the possible modelsof the finite directory of models.

Subsequently the method includes a step 86 of summation of all theproducts previously determined for all the possible models λ₁ to λ_(I)of the finite directory of models.

In this way a confidence index is determined for a given acousticstring.

The method then includes a step 88 of combination of the confidenceindices of each string of the given acoustic segment in order to supplythe confidence index I_(n) of the model H_(n) under consideration.

The relation defining I_(n) can be reduced to the following algorithmicequation:

$I_{n} = {{\sum\limits_{t = {b{(n)}}}^{e{(n)}}\left\lbrack {\log\;{P\left( O_{t} \right.}{\overset{\sim}{Q}}_{t}} \right)} + {\log{\overset{\_}{d}\left( {\overset{\sim}{Q}}_{t} \right)}} - {\log{\overset{\_}{d}\left( H_{n} \right)}} + {\log\;{P\left( H_{n} \right)}} - {\log\left\lbrack {\sum\limits_{i = 1}^{I}\;{\sum\limits_{j = 1}^{J{(i)}}{P\left( {O_{t}\left. {Q_{t} = q_{j}^{i}} \right)\frac{\overset{\_}{d}\left( q_{j}^{i} \right)}{\overset{\_}{d}\left( \lambda_{i} \right)}{P\left( \lambda_{i} \right)}} \right\rbrack}}} \right\rbrack}}$

Therefore the sub-steps 82, 84 and 86 of step 80 may be described in analgorithmic manner in the following form:

An accumulator PO is defined, then the following calculations arecarried out: PO=0;

For each model λ_(i) of the directory, with i being between 1 and I, andfor each state j of the model λ_(i) with j being between 1 and J(i), thefollowing calculations are made:

${\left. {{P\; O} = {{P\; O} + {P\left( O_{t} \right.q_{j}^{i}}}} \right)\frac{\overset{\_}{d}\left( q_{j}^{i} \right)}{\overset{\_}{d}\left( \lambda_{i} \right)}{P\left( \lambda_{i} \right)}};$$I_{n} = {I_{n} + {\log\;{P\left( {{O_{t}\left. {\overset{\sim}{Q}}_{t} \right)};{I_{n} = {I_{n} + {\log\;{\overset{\_}{d}\left( q_{t}^{n} \right)}}}};{I_{n} = {I_{n} - {\log\;{\overset{\_}{d}\left( H_{n} \right)}}}};{I_{n} = {I_{n} + {\log\;{P\left( H_{n} \right)}}}};{I_{n} = {I_{n} - {P\;{O.}}}}} \right.}}}$

In the preceding relations the previous index value I_(n) issuccessively updated to the current value.

The method then advantageously includes a sub-step 90 of standardisationof the alignment confidence index in order to deliver a confidence indexwhich is standardised relative to the total duration of the model.

Thus from the probability of models P_(m) of the a priori modelprobability P(λ_(i)) and the average duration of occupancy of thenon-observable states d(q_(j) ^(i)) the step 80 delivers the alignmentconfidence index I_(n).

This confidence index is very reliable due to the fact in particularthat it is calculated from a priori value probabilities P_(vp) estimatedin an analytical manner from the average duration of occupancy of thenon-observable states of the process, thus making it possible to takeinto account the time passed in each of the hidden states and then totake into account the temporal characteristics of the sequence of modelsH₁ ^(N).

Subsequently the method includes a step 100 of local modification of thesequence H₁ ^(N) as a function of the alignment confidence indices I_(n)determined for each model of the sequence.

Thus during the step 100 a decision permitting a model to be eitheraccepted or rejected is taken for each model of the sequence H₁ ^(N).

In the described embodiment, this decision is taken automatically as afunction of the confidence index I_(n) determined during the step 80 anda functioning point considered as a threshold for acceptance orrejection of the model.

When a model is rejected, the step 100 of local modification thenalternatively includes a sub-step of deletion, substitution or insertionof a model.

Thus one or more models of the sequence of models H₁ ^(N) may bemanually or automatically deleted, replaced or one or more new modelsmay be interposed between two models of the sequence.

Advantageously the sequence thus modified is then used again in themethod in order to be re-aligned with the sequence of acoustic stringsO₁ ^(T) during the step 60 and to give rise to a new calculation of aconfidence index for each association between a model and an acousticsegment during the step 80.

The steps 60, 80 and 100 are repeated until there is no longer anyrejected model or until there are no longer any possible modificationssuch that the delivered sequence of labelled strings corresponds to thebest possible hypothesis of decoding.

It is therefore apparent that the method according to the inventionpermits the definition of a confidence index with increased precision.The use of this index makes it possible in particular to automate thewhole of the method of processing of a speech signal, to defineautomatic modifications and to obtain an optimised result.

The described method may be implemented by software and/or hardwaremeans such as computers, microprocessors or any other adapted equipment.

The described method may for example be used in systems for voicesynthesis or for forming learning databases for voice recognitionsystems and, due to the use of a very precise confidence index and therelooping after an automatic modification, makes it possible to obtainsequences of labelled strings which are reliable and can be exploiteddirectly without requiring human intervention.

Within the framework of voice synthesis, as has been described, thesequence of models and the sequence of acoustic strings correspond tothe same text.

As a variant, the method according to the invention can be used in voicerecognition systems for example in order to form databases ofrecognition samples or to permit recognition of a statement in adirectory of sequences of models.

For example, the predetermined likely model sequences are alignedsuccessively with the sequence of acoustic strings known as the targetsequence and the confidence indices of each model are combined over thewhole of the sequence in order to deliver a measurement of similaritybetween the sequence of models and the sequence of acoustic strings. Theassociation with the highest measurement of similarity is retained.

Alternatively, the selected sequence is obtained by modification at eachrelooping in a similar manner to the previously described embodiment.

Finally, in the example described the hidden Markov models are models ofwhich the non-observable processes have discrete states. However, themethod may also be carried out with models of which the non-observableprocesses have continuous values.

1. Method of automatic processing of a speech signal comprising: anautomatic step of determination of at least one sequence of probabilitymodels coming from a finite directory of models, each sequencedescribing the probability of acoustic production of a sequence ofsymbolic units of a phonological nature coming from a finite alphabet,the said sequence of symbolic units corresponding to at least one giventext and the said probability models each including an observable randomprocess corresponding to the acoustic production of symbolic units and anon-observable random process having known probability properties,so-called Markov properties; a step of processing a speech signal todetermine a sequence of digital data strings, known as acoustic strings,representing acoustic properties of the speech signal; a step ofalignment between the said sequence of acoustic strings and the said atleast one sequence of models, each model being associated with asub-sequence of acoustic strings, forming an acoustic segment, and eachvalue of the non-observable process of each model being associated witha sub-sequence of acoustic strings forming an acoustic sub-segment inorder to deliver a sequence of non-observable process values associatinga value with each acoustic string, known as an aligned sequence; a stepof determination of a confidence index of acoustic alignment for eachassociation between a model of the sequence and an acoustic segment,known as a model alignment confidence index, and corresponding to anestimate of the probability a posteriori of the model given theobservation of the corresponding acoustic segment, known as the aposteriori model probability, said step of determination of a confidenceindex of acoustic alignment providing data of the confidence index ofacoustic alignment, characterised in that each step of determination ofan alignment confidence index for a model comprises the calculation ofthe value of the said index at least from a combination of: theprobability of observation of each acoustic string given the value ofthe non-observable process, known as the model probability anddetermined from known characteristic parameters of the probabilitymodel; probabilities of production a priori of all the models of thesaid directory, independently of one another, known as the a priorimodel probabilities; and the analytical estimation of the averageduration of occupancy of the values of the non-observable process of themodel; and a step of delivering a final sequence of labeled stringscomprised of speech data.
 2. Method as claimed in claim 1, characterisedin that each step of determination of an acoustic confidence index for amodel includes a sub-step of determination of the estimate of the apriori probability of each value of the non-observable process of themodel, known as the a priori value probability, carried out on the basisof the said analytical estimation of the average duration of occupancyof the values of the non-observable process of the model.
 3. Method asclaimed in claim 1, characterised in that each step of determination ofan alignment confidence index for a model includes a sub-step ofdetermination of a confidence index for each acoustic string forming theacoustic segment associated with the said model and a sub-step ofcombination of the confidence indices of each string of the said segmentin order to deliver the said confidence index of the said model. 4.Method as claimed in claim 3, characterised in that each sub-step ofdetermination of a confidence index for a given string includes: asub-step of initial calculation combining the model probability, the apriori model probability of the model in progress and the averageduration of occupancy of the non-observable values for all the values ofthe non-observable process of the said aligned sequence and of the modelin progress; a sub-step of calculation of the product of the modelprobability, the a priori model probability and the a priori valueprobability, produced for each value of the non-observable process ofall the possible models in the said finite directory of models; and asub-step of summation of all the said products for all the possiblemodels of the said finite directory of models in order to deliver thesaid confidence index of the said given acoustic string from the resultsof the said sub-steps.
 5. Method as claimed in claim 1, characterised inthat it includes a sub-step of standardisation of the confidence indicesby model as a function of the duration of the models.
 6. Method asclaimed in claim 1, characterised in that the said automatic step ofdetermination of a sequence of probability models corresponding to agiven text includes: a sub-step of acquisition of a graphemicrepresentation of the said given text; a sub-step of determination ofthe said sequence of symbolic units from the said graphemicrepresentation; and an automatic sub-step of modeling of the saidsequence of symbolic units by its breakdown on a base of the saidprobability models in order to deliver the said sequence of probabilitymodels.
 7. Method as claimed in claim 6, characterised in that the saidmodeling sub-step associates a single probability model with eachsymbolic unit of the said sequence of symbolic units.
 8. Method asclaimed in claim 1, characterised in that the said step of determinationof a sequence of digital strings includes: a sub-step of acquisition ofa speech signal corresponding to the diction of the said given text,adapted in order to deliver a sequence of digital samples of the saidspeech signal; and a sub-step of spectral analysis of the said samplesin order to deliver a breakdown of the frequency spectrum of the saidspeech signal on a non-linear scale, the said breakdown forming the saidsequence of acoustic strings.
 9. Method as claimed in claim 8,characterised in that the said sub-step of spectral analysis correspondsto a sub-step of Fourier transformation of the said speech signal, ofdetermination of the distribution of its energy on a non-linear scale byfiltering, and of transformation into cosine.
 10. Method as claimed inclaim 1, characterised in that the said step of alignment between thesaid sequence of acoustic strings and the said sequence of modelsincludes: a sub-step of calculation of a plurality of possiblealignments each associated with a relevance index; and a sub-step ofselection of a single alignment amongst the said plurality of possiblealignments.
 11. Method as claimed in claim 10, characterised in that thesaid sub-step of determination of a plurality of possible alignmentscomprises the calculation of at least one optimum alignment, asdetermined by a so-called Viterbi algorithm.
 12. Method as claimed inclaim 1, characterised in that it also includes a step of localmodification of the said sequence of models as a function of the saidalignment confidence indices determined for each model of the saidsequence of models.
 13. Method as claimed in claim 12, characterised inthat the said step of local modification comprises a sub-step ofdeletion of a model from the said sequence of models.
 14. Method asclaimed in claim 12, characterised in that the said step of localmodification includes a sub-step of substitution of a model of the saidsequence of models by another model.
 15. Method as claimed in claim 12,characterised in that the said step of local modification includes asub-step of insertion of a model between two models of the said sequenceof models.
 16. Method as claimed in claim 12, characterised in that thesaid steps of alignment and of calculation of a confidence index arerepeated after each step of local modification of the said sequence ofmodels.
 17. Method as claimed in claim 1, characterised in that the saidstep of determination of at least one sequence of models is adapted forthe determination of a sequence of models corresponding to a given text,and in that the said sequence of acoustic strings represents propertiesof a speech signal corresponding to the locution of the said same giventext.
 18. Method as claimed in claim 1, characterised in that the saidstep of determination of sequences of models is adapted for thedetermination of a plurality of sequences of models each correspondingto a given text, and in that the said sequence of acoustic stringsrepresents properties of a speech signal corresponding to the locutionof any text whatsoever, the said method including a step of selection ofone or several sequences of models amongst the said plurality forcarrying out the said step of determination of confidence indices. 19.Method as claimed in claim 1, characterised in that the said models aremodels of which the observable processes have discrete values, thevalues of the non-observable processes being the states of theseprocesses.
 20. Method as claimed in claim 1, characterised in that thesaid models are models of which the nonobservable processes havecontinuous values.
 21. Method as claimed in claim 1, wherein, saidautomatic step of determination of at least one sequence of probabilitymodels comprises: i) acquisition of a symbolic representation of the atleast one given text in one of a graphemic representation and anorthographic representation, ii) with reference to a database,determination of a sequence of symbolic units of the phonological natureof the finite alphabet from the said symbolic representation, and iii)modeling the determined sequence of phonetic symbolic units by abreakdown on a base of probability models of hidden Markov models, saidstep of processing a speech signal to determine a sequence of digitaldata strings, includes acquiring said speech signal and said acquiredspeech signal corresponding to said at least one given text, and ananalysis carried out on a Hamming sliding window, said step of alignmentbetween the said sequence of acoustic strings and the said at least onesequence of models applies a Viterbi algorithm, and further comprisingthe step of processing a speech signal by applying the confidence indexas part of a system for one of voice synthesis and voice recognition.